-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\knm4642\Dropbox\Nha Meo Heo Beo\_JMP\WORKING FOLDER\Table A16_Corporate trust culture robustness checks.log
  log type:  text
 opened on:  18 Feb 2026, 23:54:06

. 
. ///PREPARE DATA
> use "Employee review trust sentiment sample.dta", clear

. * construct time period variables
. cap drop timesinceCEO* timeperiod

. gen timesinceCEO = min(year - termstartyr, 9)

. gen timesinceCEO2 = timesinceCEO^2

. gen timesinceCEO3 = timesinceCEO^3

. gen timeperiod = 1 if inrange(timesinceCEO, 0, 2)
(466,700 missing values generated)

. replace timeperiod = 2 if inrange(timesinceCEO, 3, 4)
(111,975 real changes made)

. replace timeperiod = 3 if inrange(timesinceCEO, 5, 6)
(91,893 real changes made)

. replace timeperiod = 4 if inrange(timesinceCEO, 7, 8)
(81,933 real changes made)

. replace timeperiod = 5 if timesinceCEO >= 9
(180,899 real changes made)

. * compute corporate trust culture measures
. cap drop trustsentiment

. gen trustsentiment = (trustsentiment_pos == 1) - (trustsentiment_neg == 1)

. cap drop trust_mtoe trust_etom

. gen trustsentiment_mtoe = ///
>         (trustsentiment_pos == 1) * (trustdirection_mtoe == 1) ///
>         - (trustsentiment_neg == 1) * (trustdirection_mtoe == 1)

. gen trustsentiment_etom = ///
>         (trustsentiment_pos == 1) * (trustdirection_etom == 1) ///
>         - (trustsentiment_neg == 1) * (trustdirection_etom == 1) 

. foreach var in trustsentiment trustsentiment_mtoe trustsentiment_etom {
  2.         replace `var' = . if trustsentiment_pos == .
  3. }
(84,323 real changes made, 84,323 to missing)
(84,323 real changes made, 84,323 to missing)
(84,323 real changes made, 84,323 to missing)

. gen trustsentiment_mtoe_pos = (trustsentiment_pos == 1) * (trustdirection_mtoe == 1)

. gen trustsentiment_mtoe_neg = (trustsentiment_neg == 1) * (trustdirection_mtoe == 1)

. gen trustsentiment_etom_pos = (trustsentiment_pos == 1) * (trustdirection_etom == 1)

. gen trustsentiment_etom_neg = (trustsentiment_neg == 1) * (trustdirection_etom == 1)

. gen trustsentiment_abs = trustsentiment_pos | trustsentiment_neg

. gen trustsentiment_mtoe_abs = trustsentiment_mtoe_pos | trustsentiment_mtoe_neg

. gen trustsentiment_etom_abs = trustsentiment_etom_pos | trustsentiment_etom_neg

. foreach var in trustsentiment trustsentiment_mtoe trustsentiment_etom {
  2.         replace `var' = `var' * 100
  3.         replace `var'_pos = `var'_pos * 100
  4.         replace `var'_neg = `var'_neg * 100
  5.         replace `var'_abs = `var'_abs * 100
  6. }
(5,389 real changes made)
(756 real changes made)
(4,645 real changes made)
(89,718 real changes made)
(2,534 real changes made)
(540 real changes made)
(2,004 real changes made)
(2,539 real changes made)
(1,229 real changes made)
(89 real changes made)
(1,148 real changes made)
(1,233 real changes made)

. * compute review counts
. foreach var in trustsentiment approveceo iscurrentjob reviewgap firmage ///
>         trust_sd gender age yrinco education !nonUS ///
>         highincome_sd getahead_sd risktakinggps_sd patiencegps_sd {
  2.         keep if `var' != .
  3. }
(84,323 observations deleted)
(141,117 observations deleted)
(0 observations deleted)
(0 observations deleted)
(0 observations deleted)
(55,318 observations deleted)
(0 observations deleted)
(172 observations deleted)
(0 observations deleted)
(7,735 observations deleted)
(0 observations deleted)
(0 observations deleted)
(0 observations deleted)
(0 observations deleted)
(0 observations deleted)

. *** all reviews
. cap drop *_all

. bysort boardid ceoid termstartyr: gen revcnt_all = _N

. bysort boardid ceoid termstartyr: egen revcnt_allcur = sum(iscurrentjob)

. gen w_all = 1/revcnt_all

. gen w_allcur = 1/revcnt_allcur
(324 missing values generated)

. *** R&D workers only
. replace jobtitle = upper(jobtitle)
(233,236 real changes made)

. cap drop RDworker *_rd

. gen RDworker = ///
>         strpos(jobtitle, "ENGINEER") ///
>         | strpos(jobtitle, "SCIEN") /// 
>         | strpos(jobtitle, "RESEARCH") ///
>         | strpos(jobtitle, "TECHNOLOGY DEVELOP") ///
>         | strpos(jobtitle, "R&D") ///
>         | strpos(jobtitle, "R& D") ///
>         | strpos(jobtitle, "R &D") ///
>         | strpos(jobtitle, "R & D") ///
>         | strpos(jobtitle, "RDD ") 

. bysort boardid ceoid termstartyr: egen revcnt_rd = sum(RDworker == 1)

. bysort boardid ceoid termstartyr: egen revcnt_rdcur = sum(RDworker == 1 & iscurrentjob)

. gen w_rd = 1/revcnt_rd
(22,057 missing values generated)

. gen w_rdcur = 1/revcnt_rdcur
(35,690 missing values generated)

. * set globals
. global review = "approveceo iscurrentjob reviewgap"

. global firm = "firmage firmage2"

. global ceo = "gender age age2 yrinco i.education nonUS"

. global culture = "highincome_sd getahead_sd risktakinggps_sd patiencegps_sd"

. global reviewsample = "revcnt_all > 49"

. global rdreviewsample = "RDworker & revcnt_rd > 24 & revcnt_all > 49"

. global cluster = "mainethcode"

. 
. 
. //PREPARE TABLE
. * PANEL A: All employees
. eststo clear

. eststo col1: /// baseline
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${reviewsample} [aw = w_all], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =    331,091
Absorbing 2 HDFE groups                           F(  17,     26) =      72.75
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0038
                                                  Adj R-squared   =     0.0030
                                                  Within R-sq.    =     0.0005
Number of clusters (mainethcode) =         27     Root MSE        =     8.0122

                               (Std. err. adjusted for 27 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .3427372   .1595902     2.15   0.041     .0146949    .6707795
      approveceo |    .251513   .0498113     5.05   0.000     .1491244    .3539016
iscurrentjobflag |   .0732315   .0669139     1.09   0.284    -.0643119    .2107749
       reviewgap |   .0706278   .0219524     3.22   0.003      .025504    .1157515
         firmage |          0  (omitted)
        firmage2 |   .0001955   .0002765     0.71   0.486     -.000373    .0007639
          gender |  -.1529438   .0993048    -1.54   0.136    -.3570677    .0511801
             age |  -.0462694    .053647    -0.86   0.396    -.1565425    .0640037
            age2 |   .0004159   .0004849     0.86   0.399    -.0005809    .0014126
          yrinco |    .003683     .00404     0.91   0.370    -.0046213    .0119874
                 |
       education |
              2  |   .0966902   .1776636     0.54   0.591    -.2685025    .4618828
              3  |   .0055747   .1821156     0.03   0.976    -.3687693    .3799186
              4  |   .5776689   .2572242     2.25   0.033      .048937    1.106401
                 |
           nonUS |   .0875328   .1452886     0.60   0.552    -.2111122    .3861778
   highincome_sd |  -.0909611   .0667439    -1.36   0.185    -.2281552    .0462329
     getahead_sd |   .0231381   .0776647     0.30   0.768    -.1365039    .1827801
risktakinggps_sd |   .0982046   .0668106     1.47   0.154    -.0391265    .2355357
  patiencegps_sd |  -.1920496   .1300042    -1.48   0.152    -.4592771    .0751779
           _cons |  -.7982814    2.17725    -0.37   0.717    -5.273682    3.677119
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       266           0         266     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos | 331,091  398.000001    .1334494    3.65064          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .13344938

.                 sum trustsentiment_mtoe_neg [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg | 331,091  398.000001    .5126909   7.141876          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .5126909

.                 sum trustsentiment_mtoe_abs [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs | 331,091  398.000001    .6457217   8.009707          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .6457217

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .53078161

.                 estadd local spec           "Baseline"

added macro:
               e(spec) : "Baseline"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     331091        266

.                 eststo col1, addscalars(nofirms r(ndistinct))   
(e(nofirms) = 266 added)

. eststo col2: /// additional fixed effects
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${reviewsample} [aw = w_all], a(boardid reviewyear year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 14 iterations)
note: reviewgap is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: firmage is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =    331,091
Absorbing 3 HDFE groups                           F(  16,     26) =      49.55
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0040
                                                  Adj R-squared   =     0.0031
                                                  Within R-sq.    =     0.0005
Number of clusters (mainethcode) =         27     Root MSE        =     8.0118

                               (Std. err. adjusted for 27 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .3473167    .154124     2.25   0.033     .0305102    .6641232
      approveceo |   .2507654   .0505255     4.96   0.000     .1469087    .3546222
iscurrentjobflag |   .0764012   .0618719     1.23   0.228    -.0507784    .2035807
       reviewgap |          0  (omitted)
         firmage |          0  (omitted)
        firmage2 |   .0001808   .0002973     0.61   0.549    -.0004304     .000792
          gender |  -.1661992   .0952992    -1.74   0.093    -.3620895    .0296912
             age |  -.0592784   .0584588    -1.01   0.320    -.1794422    .0608853
            age2 |   .0005239   .0005225     1.00   0.325    -.0005502    .0015979
          yrinco |   .0033626   .0042132     0.80   0.432    -.0052978     .012023
                 |
       education |
              2  |   .0700371   .1749206     0.40   0.692    -.2895173    .4295916
              3  |  -.0323605   .1786687    -0.18   0.858    -.3996194    .3348983
              4  |   .5470598   .2498609     2.19   0.038     .0334633    1.060656
                 |
           nonUS |   .0642389   .1355617     0.47   0.640    -.2144122    .3428899
   highincome_sd |  -.1013934   .0645637    -1.57   0.128    -.2341059    .0313192
     getahead_sd |   .0350091   .0746633     0.47   0.643    -.1184635    .1884816
risktakinggps_sd |   .1052799   .0645646     1.63   0.115    -.0274346    .2379944
  patiencegps_sd |  -.2082591    .120238    -1.73   0.095    -.4554119    .0388936
           _cons |  -.3297551   2.429336    -0.14   0.893    -5.323326    4.663816
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       266           0         266     |
  reviewyear |        10           1           9     |
        year |        11           1          10    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher

.                 sum trustsentiment_mtoe_pos [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos | 331,091  398.000001    .1334494    3.65064          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .13344938

.                 sum trustsentiment_mtoe_neg [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg | 331,091  398.000001    .5126909   7.141876          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .5126909

.                 sum trustsentiment_mtoe_abs [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs | 331,091  398.000001    .6457217   8.009707          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .6457217

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .53787369

.                 estadd local spec           "Add FEs"

added macro:
               e(spec) : "Add FEs"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 estadd local yearFE         "X"

added macro:
             e(yearFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     331091        266

.                 eststo col2, addscalars(nofirms r(ndistinct))
(e(nofirms) = 266 added)

. eststo col3: /// positive reviews
>         reghdfe trustsentiment_mtoe_pos trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${reviewsample} [aw = w_all], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =    331,091
Absorbing 2 HDFE groups                           F(  17,     26) =      75.18
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0036
                                                  Adj R-squared   =     0.0027
                                                  Within R-sq.    =     0.0004
Number of clusters (mainethcode) =         27     Root MSE        =     3.6457

                               (Std. err. adjusted for 27 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsenti~e_pos | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .1116917   .0452562     2.47   0.020     .0186663    .2047172
      approveceo |   .0916808   .0251248     3.65   0.001      .040036    .1433256
iscurrentjobflag |   .0096582   .0235084     0.41   0.685    -.0386641    .0579804
       reviewgap |    .008091   .0132752     0.61   0.547    -.0191966    .0353786
         firmage |          0  (omitted)
        firmage2 |   .0000265   .0001102     0.24   0.812       -.0002    .0002529
          gender |   .0275617   .0829979     0.33   0.742     -.143043    .1981664
             age |  -.0809687   .0449232    -1.80   0.083    -.1733097    .0113723
            age2 |   .0007282   .0003737     1.95   0.062      -.00004    .0014964
          yrinco |   .0002443   .0011282     0.22   0.830    -.0020748    .0025633
                 |
       education |
              2  |  -.0400348   .0382126    -1.05   0.304    -.1185821    .0385124
              3  |   .0495955   .0485429     1.02   0.316    -.0501858    .1493768
              4  |   .1959891   .1005117     1.95   0.062    -.0106157    .4025938
                 |
           nonUS |   .0413265   .0464656     0.89   0.382     -.054185     .136838
   highincome_sd |  -.1204413   .0505358    -2.38   0.025    -.2243192   -.0165634
     getahead_sd |   .0863703   .0349216     2.47   0.020      .014588    .1581527
risktakinggps_sd |   .0338259   .0144451     2.34   0.027     .0041336    .0635182
  patiencegps_sd |  -.1001038    .039282    -2.55   0.017     -.180849   -.0193585
           _cons |   1.761687    1.46111     1.21   0.239    -1.241667     4.76504
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       266           0         266     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos | 331,091  398.000001    .1334494    3.65064          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .13344938

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .83695961

.                 sum trustsentiment_mtoe_neg [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg | 331,091  398.000001    .5126909   7.141876          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .5126909

.                 sum trustsentiment_mtoe_abs [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs | 331,091  398.000001    .6457217   8.009707          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .6457217

.                 estadd local spec           "Pos reviews"

added macro:
               e(spec) : "Pos reviews"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     331091        266

.                 eststo col3, addscalars(nofirms r(ndistinct))
(e(nofirms) = 266 added)

. eststo col4: /// negative reviews
>         reghdfe trustsentiment_mtoe_neg trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${reviewsample} [aw = w_all], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =    331,091
Absorbing 2 HDFE groups                           F(  17,     26) =     268.91
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0040
                                                  Adj R-squared   =     0.0031
                                                  Within R-sq.    =     0.0004
Number of clusters (mainethcode) =         27     Root MSE        =     7.1306

                               (Std. err. adjusted for 27 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsenti~e_neg | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |  -.2310455   .1283182    -1.80   0.083    -.4948073    .0327164
      approveceo |  -.1598322   .0341579    -4.68   0.000    -.2300448   -.0896197
iscurrentjobflag |  -.0635734   .0681941    -0.93   0.360    -.2037482    .0766015
       reviewgap |  -.0625367   .0188827    -3.31   0.003    -.1013507   -.0237228
         firmage |          0  (omitted)
        firmage2 |   -.000169   .0002144    -0.79   0.438    -.0006098    .0002718
          gender |   .1805054   .0931935     1.94   0.064    -.0110565    .3720674
             age |  -.0346993   .0453533    -0.77   0.451    -.1279243    .0585257
            age2 |   .0003123   .0003945     0.79   0.436    -.0004985    .0011232
          yrinco |  -.0034388   .0041269    -0.83   0.412    -.0119218    .0050443
                 |
       education |
              2  |   -.136725    .149641    -0.91   0.369    -.4443164    .1708664
              3  |   .0440209   .1467123     0.30   0.767    -.2575507    .3455924
              4  |  -.3816798   .1991972    -1.92   0.066    -.7911355    .0277759
                 |
           nonUS |  -.0462064   .1653899    -0.28   0.782    -.3861702    .2937575
   highincome_sd |  -.0294802   .0794779    -0.37   0.714    -.1928494     .133889
     getahead_sd |   .0632322   .0530248     1.19   0.244    -.0457618    .1722263
risktakinggps_sd |  -.0643788   .0631463    -1.02   0.317    -.1941779    .0654204
  patiencegps_sd |   .0919458   .1054299     0.87   0.391    -.1247684    .3086601
           _cons |   2.559968    2.24392     1.14   0.264    -2.052476    7.172412
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       266           0         266     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos | 331,091  398.000001    .1334494    3.65064          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .13344938

.                 sum trustsentiment_mtoe_neg [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg | 331,091  398.000001    .5126909   7.141876          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .5126909

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  -.45065255

.                 sum trustsentiment_mtoe_abs [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs | 331,091  398.000001    .6457217   8.009707          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .6457217

.                 estadd local spec           "Neg reviews"

added macro:
               e(spec) : "Neg reviews"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     331091        266

.                 eststo col4, addscalars(nofirms r(ndistinct))
(e(nofirms) = 266 added)

. eststo col5: /// current reviews
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${reviewsample} & iscurrentjob [aw = w_allcur], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: iscurrentjobflag is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: firmage is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: iscurrentjobflag omitted because of collinearity

HDFE Linear regression                            Number of obs   =    179,645
Absorbing 2 HDFE groups                           F(  15,     26) =      13.23
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0072
                                                  Adj R-squared   =     0.0056
                                                  Within R-sq.    =     0.0009
Number of clusters (mainethcode) =         27     Root MSE        =     8.1182

                               (Std. err. adjusted for 27 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .6536204   .1906209     3.43   0.002     .2617935    1.045447
      approveceo |   .3084521   .0884941     3.49   0.002     .1265499    .4903543
iscurrentjobflag |          0  (omitted)
       reviewgap |          0  (omitted)
         firmage |          0  (omitted)
        firmage2 |   .0008608   .0002309     3.73   0.001     .0003861    .0013355
          gender |  -.4904811   .1785361    -2.75   0.011    -.8574673   -.1234949
             age |  -.0633583   .0826506    -0.77   0.450    -.2332491    .1065324
            age2 |   .0005491   .0006827     0.80   0.428    -.0008541    .0019524
          yrinco |    .013803   .0044854     3.08   0.005     .0045832    .0230229
                 |
       education |
              2  |   .1186636   .1916438     0.62   0.541    -.2752659    .5125931
              3  |    .195516   .2741568     0.71   0.482    -.3680214    .7590533
              4  |   .7874275   .2660902     2.96   0.006     .2404713    1.334384
                 |
           nonUS |  -.0269209   .2707875    -0.10   0.922    -.5835327    .5296909
   highincome_sd |  -.2384358    .137022    -1.74   0.094    -.5200885    .0432169
     getahead_sd |   .0737879   .0973331     0.76   0.455    -.1262832     .273859
risktakinggps_sd |   .1725983   .1545299     1.12   0.274    -.1450425    .4902391
  patiencegps_sd |  -.4145821   .1577961    -2.63   0.014    -.7389365   -.0902276
           _cons |  -2.075022   2.417867    -0.86   0.399     -7.04502    2.894975
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       266           0         266     |
  reviewyear |         9           1           8     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_allcur] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos | 179,645  397.000001    .1375021   3.705588          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .13750212

.                 sum trustsentiment_mtoe_neg [aw = w_allcur] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg | 179,645  397.000001    .5271237   7.241188          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .52712372

.                 sum trustsentiment_mtoe_abs [aw = w_allcur] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs | 179,645  397.000001    .6644486   8.124268          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .66444858

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .98370355

.                 estadd local spec           "Current revs"

added macro:
               e(spec) : "Current revs"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     179645        266

.                 eststo col5, addscalars(nofirms r(ndistinct))
(e(nofirms) = 266 added)

. eststo col6: /// review count > 9
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if revcnt_all > 9 [aw = w_all], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =    332,276
Absorbing 2 HDFE groups                           F(  17,     26) =      96.02
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0063
                                                  Adj R-squared   =     0.0054
                                                  Within R-sq.    =     0.0007
Number of clusters (mainethcode) =         27     Root MSE        =     8.1251

                               (Std. err. adjusted for 27 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .3585779   .1383813     2.59   0.015     .0741311    .6430248
      approveceo |   .2709417   .0861851     3.14   0.004     .0937857    .4480977
iscurrentjobflag |   .0578717   .0670925     0.86   0.396    -.0800388    .1957823
       reviewgap |   .1012209   .0279263     3.62   0.001     .0438176    .1586243
         firmage |          0  (omitted)
        firmage2 |   .0003826   .0002564     1.49   0.148    -.0001445    .0009096
          gender |   .0649325    .162563     0.40   0.693    -.2692206    .3990856
             age |  -.0199786    .051681    -0.39   0.702    -.1262104    .0862531
            age2 |   .0001332   .0004382     0.30   0.764    -.0007676     .001034
          yrinco |   .0146696   .0061717     2.38   0.025     .0019835    .0273557
                 |
       education |
              2  |   .1068401   .1701074     0.63   0.535    -.2428208     .456501
              3  |   .0561584   .2016416     0.28   0.783    -.3583218    .4706385
              4  |    .602024   .1622729     3.71   0.001     .2684673    .9355808
                 |
           nonUS |   .1135524   .1826469     0.62   0.540    -.2618838    .4889885
   highincome_sd |   .0579805   .1106915     0.52   0.605    -.1695492    .2855102
     getahead_sd |   .1462997   .0933043     1.57   0.129      -.04549    .3380895
risktakinggps_sd |   .0408808   .0784544     0.52   0.607    -.1203844    .2021461
  patiencegps_sd |  -.3345524   .1243003    -2.69   0.012    -.5900553   -.0790496
           _cons |  -3.912273   2.433533    -1.61   0.120    -8.914471    1.089925
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       269           0         269     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos | 332,276  439.000001    .1324399   3.636825          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .13243995

.                 sum trustsentiment_mtoe_neg [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg | 332,276  439.000001    .5336947   7.285932          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .5336947

.                 sum trustsentiment_mtoe_abs [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs | 332,276  439.000001    .6657552   8.132188          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .66575516

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .53860331

.                 estadd local spec           "Alt cutoff"

added macro:
               e(spec) : "Alt cutoff"

.                 estadd local cutoff         "10"

added macro:
             e(cutoff) : "10"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     332276        269

.                 eststo col6, addscalars(nofirms r(ndistinct))
(e(nofirms) = 269 added)

. eststo col7: /// review count > 24
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if revcnt_all > 24 [aw = w_all], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =    332,042
Absorbing 2 HDFE groups                           F(  17,     26) =      56.90
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0057
                                                  Adj R-squared   =     0.0048
                                                  Within R-sq.    =     0.0006
Number of clusters (mainethcode) =         27     Root MSE        =     8.1754

                               (Std. err. adjusted for 27 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .4306543   .1435525     3.00   0.006     .1355778    .7257308
      approveceo |   .2348621   .0648436     3.62   0.001     .1015742      .36815
iscurrentjobflag |   .0975953   .0660788     1.48   0.152    -.0382317    .2334223
       reviewgap |   .0918207   .0319165     2.88   0.008     .0262154    .1574261
         firmage |          0  (omitted)
        firmage2 |   .0003443   .0003077     1.12   0.273    -.0002881    .0009767
          gender |     .10004   .1865865     0.54   0.596    -.2834941    .4835741
             age |  -.0231737   .0569455    -0.41   0.687    -.1402268    .0938794
            age2 |   .0001263   .0004891     0.26   0.798    -.0008791    .0011317
          yrinco |   .0107127   .0059663     1.80   0.084    -.0015512    .0229766
                 |
       education |
              2  |   .2433734   .2601567     0.94   0.358    -.2913864    .7781331
              3  |   .1301538     .28783     0.45   0.655    -.4614892    .7217969
              4  |   .7088302    .266356     2.66   0.013     .1613276    1.256333
                 |
           nonUS |   .2443626   .1649456     1.48   0.150    -.0946879    .5834131
   highincome_sd |  -.0323989   .1114513    -0.29   0.774    -.2614903    .1966926
     getahead_sd |   .1608104   .0804477     2.00   0.056    -.0045522    .3261729
risktakinggps_sd |   .0985512   .0691231     1.43   0.166    -.0435334    .2406357
  patiencegps_sd |  -.3620553   .1258729    -2.88   0.008    -.6207907   -.1033198
           _cons |  -3.659221   2.405526    -1.52   0.140    -8.603851    1.285409
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       268           0         268     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos | 332,042  424.000001    .1371253   3.700509          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .13712532

.                 sum trustsentiment_mtoe_neg [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg | 332,042  424.000001    .5368521   7.307336          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .53685214

.                 sum trustsentiment_mtoe_abs [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs | 332,042  424.000001    .6735846   8.179544          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .67358455

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .63934704

.                 estadd local spec           "Alt cutoff"

added macro:
               e(spec) : "Alt cutoff"

.                 estadd local cutoff         "25"

added macro:
             e(cutoff) : "25"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     332042        268

.                 eststo col7, addscalars(nofirms r(ndistinct))
(e(nofirms) = 268 added)

. eststo col8: /// review count > 99
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if revcnt_all > 99 [aw = w_all], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 6 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =    327,580
Absorbing 2 HDFE groups                           F(  17,     26) =     167.18
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0033
                                                  Adj R-squared   =     0.0024
                                                  Within R-sq.    =     0.0005
Number of clusters (mainethcode) =         27     Root MSE        =     7.9871

                               (Std. err. adjusted for 27 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .2506242   .1253366     2.00   0.056    -.0070088    .5082572
      approveceo |   .2791469    .047443     5.88   0.000     .1816264    .3766674
iscurrentjobflag |   .0998591   .0703067     1.42   0.167    -.0446585    .2443766
       reviewgap |   .0899741   .0236029     3.81   0.001     .0414576    .1384907
         firmage |          0  (omitted)
        firmage2 |   .0002574   .0002081     1.24   0.227    -.0001704    .0006852
          gender |  -.0464014   .1136346    -0.41   0.686    -.2799806    .1871778
             age |    .029304   .0473093     0.62   0.541    -.0679416    .1265496
            age2 |  -.0002636   .0004243    -0.62   0.540    -.0011357    .0006085
          yrinco |   .0040707   .0032076     1.27   0.216    -.0025227    .0106641
                 |
       education |
              2  |  -.1278145   .2051076    -0.62   0.539    -.5494193    .2937903
              3  |  -.2929635   .1630319    -1.80   0.084    -.6280804    .0421535
              4  |   .1134991   .2841223     0.40   0.693    -.4705227     .697521
                 |
           nonUS |  -.3585753   .2570506    -1.39   0.175    -.8869505    .1697998
   highincome_sd |  -.0259085    .100799    -0.26   0.799    -.2331038    .1812868
     getahead_sd |  -.0347121   .0621675    -0.56   0.581    -.1624992     .093075
risktakinggps_sd |   .0178231   .0620553     0.29   0.776    -.1097334    .1453796
  patiencegps_sd |  -.0914837   .1076011    -0.85   0.403    -.3126611    .1296936
           _cons |  -2.387558   1.292164    -1.85   0.076     -5.04364    .2685243
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       255           0         255     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos | 327,580         352    .1248108   3.530657          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .1248108

.                 sum trustsentiment_mtoe_neg [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg | 327,580         352    .5171581   7.172762          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .51715808

.                 sum trustsentiment_mtoe_abs [aw = w_all] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs | 327,580         352    .6414956   7.983623          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .6414956

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .39068734

.                 estadd local spec           "Alt cutoff"

added macro:
               e(spec) : "Alt cutoff"

.                 estadd local cutoff         "100"

added macro:
             e(cutoff) : "100"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     327580        255

.                 eststo col8, addscalars(nofirms r(ndistinct))
(e(nofirms) = 255 added)

. 
. esttab /*using "Table A16_Corporate trust culture robustness checks_Panel A.tex"*/, ///
>         cells(b(star fmt(3)) se(par fmt(3))) starlevels(* 0.1 ** 0.05 *** 0.01) ///
>         nomtitle collabels(none) label varwidth(23) modelwidth(12) ///
>         mgroups("Employee review's top down trust sentiment", pattern(1 0 0 0 0 0 0 0) ///
>                 prefix(\multicolumn{@span}{c}{) suffix(}) span) ///
>         keep(trust_sd) coeflab(trust_sd "CEO's trust") ///
>         stats(mean_pos mean_neg ratio spec cutoff  controls baselineFE yearFE N nofirms, ///
>                 fmt(%9.3fc %9.3fc %9.3fc %9.0fc %9.0fc) ///
>                 lab("Positive reviews" "Negative reviews" "Normalized effect size" "Specification" "Minimum review count" ///
>                         "Baseline controls" "Firm \& Review year FEs" "Year being reviewed FEs" "Observations" "Firms"))

-------------------------------------------------------------------------------------------------------------------------------------------------------
                        \multicolumn{8}{c}{Employee review's top down trust sentiment}                                                                 
                                 (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)   
-------------------------------------------------------------------------------------------------------------------------------------------------------
CEO's trust                    0.343**         0.347**         0.112**        -0.231*          0.654***        0.359**         0.431***        0.251*  
                             (0.160)         (0.154)         (0.045)         (0.128)         (0.191)         (0.138)         (0.144)         (0.125)   
-------------------------------------------------------------------------------------------------------------------------------------------------------
Positive reviews               0.133           0.133           0.133           0.133           0.138           0.132           0.137           0.125   
Negative reviews               0.513           0.513           0.513           0.513           0.527           0.534           0.537           0.517   
Normalized effect size         0.531           0.538           0.837          -0.451           0.984           0.539           0.639           0.391   
Specification               Baseline         Add FEs     Pos reviews     Neg reviews    Current revs      Alt cutoff      Alt cutoff      Alt cutoff   
Minimum review count                                                                                              10              25             100   
Baseline controls                  X               X               X               X               X               X               X               X   
Firm \& Review year FEs            X               X               X               X               X               X               X               X   
Year being reviewed FEs                            X                                                                                                   
Observations                 331,091         331,091         331,091         331,091         179,645         332,276         332,042         327,580   
Firms                            266             266             266             266             266             269             268             255   
-------------------------------------------------------------------------------------------------------------------------------------------------------

. 
. 
. * PANEL B: R&D workers
. eststo clear

. eststo col1: /// baseline
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${rdreviewsample} [aw = w_rd], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =     24,763
Absorbing 2 HDFE groups                           F(  17,     20) =      72.53
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0167
                                                  Adj R-squared   =     0.0108
                                                  Within R-sq.    =     0.0013
Number of clusters (mainethcode) =         21     Root MSE        =     9.1695

                               (Std. err. adjusted for 21 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .7757582   .2588476     3.00   0.007     .2358117    1.315705
      approveceo |   .1857398   .3059938     0.61   0.551     -.452552    .8240317
iscurrentjobflag |   .1379408   .2929811     0.47   0.643     -.473207    .7490887
       reviewgap |    .202487   .1132439     1.79   0.089    -.0337357    .4387097
         firmage |          0  (omitted)
        firmage2 |   .0010901   .0007697     1.42   0.172    -.0005155    .0026957
          gender |  -.5153846   .2979976    -1.73   0.099    -1.136997    .1062275
             age |  -.1174957   .1873689    -0.63   0.538    -.5083403    .2733489
            age2 |   .0011259     .00165     0.68   0.503    -.0023159    .0045676
          yrinco |   -.030464   .0081839    -3.72   0.001    -.0475353   -.0133926
                 |
       education |
              2  |  -.5725745   .5666152    -1.01   0.324    -1.754513     .609364
              3  |  -.0751964   .6053628    -0.12   0.902    -1.337961    1.187568
              4  |   .8596959   .5716138     1.50   0.148    -.3326696    2.052061
                 |
           nonUS |   -.435488   .3863751    -1.13   0.273    -1.241452    .3704763
   highincome_sd |  -.6616186   .2220697    -2.98   0.007    -1.124848   -.1983892
     getahead_sd |  -.1797865   .2473596    -0.73   0.476    -.6957696    .3361965
risktakinggps_sd |   .5384662   .2679187     2.01   0.058    -.0204025    1.097335
  patiencegps_sd |  -.5317467    .240814    -2.21   0.039    -1.034076   -.0294176
           _cons |   3.624607   8.499818     0.43   0.674     -14.1057    21.35492
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       122           0         122     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos |  24,763  168.000001    .2156646   4.639053          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .21566459

.                 sum trustsentiment_mtoe_neg [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg |  24,763  168.000001    .6360647   7.950123          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .63606475

.                 sum trustsentiment_mtoe_abs [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs |  24,763  168.000001    .8517293   9.189717          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .85172934

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .91080364

.                 estadd local spec           "Baseline"

added macro:
               e(spec) : "Baseline"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      24763        122

.                 eststo col1, addscalars(nofirms r(ndistinct))
(e(nofirms) = 122 added)

. eststo col2: /// additional fixed effects
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${rdreviewsample} [aw = w_rd], a(boardid reviewyear year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 16 iterations)
note: reviewgap is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: firmage is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =     24,763
Absorbing 3 HDFE groups                           F(  16,     20) =      39.66
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0172
                                                  Adj R-squared   =     0.0109
                                                  Within R-sq.    =     0.0012
Number of clusters (mainethcode) =         21     Root MSE        =     9.1690

                               (Std. err. adjusted for 21 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .7053524   .2324488     3.03   0.007     .2204726    1.190232
      approveceo |   .1836752   .3073722     0.60   0.557     -.457492    .8248424
iscurrentjobflag |   .1536333   .2990764     0.51   0.613    -.4702292    .7774957
       reviewgap |          0  (omitted)
         firmage |          0  (omitted)
        firmage2 |    .001097    .000732     1.50   0.150    -.0004299    .0026239
          gender |  -.5333712   .3105108    -1.72   0.101    -1.181085     .114343
             age |  -.0978667   .1994549    -0.49   0.629    -.5139222    .3181888
            age2 |   .0009267   .0017582     0.53   0.604    -.0027408    .0045942
          yrinco |  -.0294495   .0085141    -3.46   0.002    -.0472096   -.0116894
                 |
       education |
              2  |  -.5769505   .5956026    -0.97   0.344    -1.819356    .6654548
              3  |  -.1154198   .6422561    -0.18   0.859    -1.455143    1.224303
              4  |   .7748223   .6015536     1.29   0.212    -.4799965    2.029641
                 |
           nonUS |  -.4379422   .3918775    -1.12   0.277    -1.255384       .3795
   highincome_sd |  -.6192693   .2435363    -2.54   0.019    -1.127277   -.1112615
     getahead_sd |  -.1695788   .2640192    -0.64   0.528    -.7203131    .3811556
risktakinggps_sd |    .555998   .2760383     2.01   0.058    -.0198078    1.131804
  patiencegps_sd |  -.5212956   .2369752    -2.20   0.040    -1.015617   -.0269741
           _cons |    3.25609   8.910163     0.37   0.719    -15.33018    21.84236
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       122           0         122     |
  reviewyear |        10           1           9     |
        year |        11           1          10    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher

.                 sum trustsentiment_mtoe_pos [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos |  24,763  168.000001    .2156646   4.639053          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .21566459

.                 sum trustsentiment_mtoe_neg [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg |  24,763  168.000001    .6360647   7.950123          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .63606475

.                 sum trustsentiment_mtoe_abs [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs |  24,763  168.000001    .8517293   9.189717          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .85172934

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .82814143

.                 estadd local spec           "Add FEs"

added macro:
               e(spec) : "Add FEs"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 estadd local yearFE         "X"

added macro:
             e(yearFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      24763        122

.                 eststo col2, addscalars(nofirms r(ndistinct))
(e(nofirms) = 122 added)

. eststo col3: /// positive reviews
>         reghdfe trustsentiment_mtoe_pos trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${rdreviewsample} [aw = w_rd], a(boardid reviewyear year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 16 iterations)
note: reviewgap is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: firmage is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =     24,763
Absorbing 3 HDFE groups                           F(  16,     20) =      67.36
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0150
                                                  Adj R-squared   =     0.0088
                                                  Within R-sq.    =     0.0013
Number of clusters (mainethcode) =         21     Root MSE        =     4.6186

                               (Std. err. adjusted for 21 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsenti~e_pos | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |    .417114   .1115222     3.74   0.001     .1844827    .6497452
      approveceo |   .2105266   .1234718     1.71   0.104    -.0470311    .4680843
iscurrentjobflag |  -.0767247   .1587081    -0.48   0.634    -.4077839    .2543346
       reviewgap |          0  (omitted)
         firmage |          0  (omitted)
        firmage2 |   .0007283   .0003299     2.21   0.039       .00004    .0014165
          gender |  -.1880264   .0640871    -2.93   0.008    -.3217097   -.0543431
             age |  -.0962194   .1334266    -0.72   0.479    -.3745424    .1821036
            age2 |   .0008492   .0011911     0.71   0.484    -.0016353    .0033337
          yrinco |  -.0101236   .0060793    -1.67   0.111    -.0228048    .0025576
                 |
       education |
              2  |  -.2376571   .2459305    -0.97   0.345    -.7506591     .275345
              3  |   -.153924   .2576824    -0.60   0.557      -.69144     .383592
              4  |   .3670954   .1955657     1.88   0.075    -.0408474    .7750383
                 |
           nonUS |  -.1552378   .1411187    -1.10   0.284    -.4496062    .1391305
   highincome_sd |  -.2282108   .1177179    -1.94   0.067    -.4737659    .0173444
     getahead_sd |  -.0202025   .0538908    -0.37   0.712    -.1326168    .0922118
risktakinggps_sd |  -.0416242   .0742446    -0.56   0.581    -.1964957    .1132473
  patiencegps_sd |   -.281422   .0749253    -3.76   0.001    -.4377133   -.1251306
           _cons |    1.88448     4.2624     0.44   0.663    -7.006731    10.77569
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       122           0         122     |
  reviewyear |        10           1           9     |
        year |        11           1          10    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher

.                 sum trustsentiment_mtoe_pos [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos |  24,763  168.000001    .2156646   4.639053          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .21566459

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  1.9340864

.                 sum trustsentiment_mtoe_neg [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg |  24,763  168.000001    .6360647   7.950123          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .63606475

.                 sum trustsentiment_mtoe_abs [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs |  24,763  168.000001    .8517293   9.189717          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .85172934

.                 estadd local spec           "Pos reviews"

added macro:
               e(spec) : "Pos reviews"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sa`mple)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |     332435        276

.                 eststo col3, addscalars(nofirms r(ndistinct))
(e(nofirms) = 276 added)

. eststo col4: /// negative reviews
>         reghdfe trustsentiment_mtoe_neg trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${rdreviewsample} [aw = w_rd], a(boardid reviewyear year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 16 iterations)
note: reviewgap is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: firmage is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)

HDFE Linear regression                            Number of obs   =     24,763
Absorbing 3 HDFE groups                           F(  16,     20) =      48.73
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0169
                                                  Adj R-squared   =     0.0107
                                                  Within R-sq.    =     0.0008
Number of clusters (mainethcode) =         21     Root MSE        =     7.9074

                               (Std. err. adjusted for 21 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsenti~e_neg | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |  -.2882384   .2258771    -1.28   0.217    -.7594099    .1829331
      approveceo |   .0268514   .2109541     0.13   0.900    -.4131912    .4668939
iscurrentjobflag |  -.2303579   .2257461    -1.02   0.320    -.7012561    .2405402
       reviewgap |          0  (omitted)
         firmage |          0  (omitted)
        firmage2 |  -.0003687   .0006691    -0.55   0.588    -.0017644     .001027
          gender |   .3453448    .302066     1.14   0.266     -.284754    .9754435
             age |   .0016473   .2233397     0.01   0.994    -.4642311    .4675257
            age2 |  -.0000775   .0020318    -0.04   0.970    -.0043157    .0041608
          yrinco |   .0193259   .0089736     2.15   0.044     .0006073    .0380446
                 |
       education |
              2  |   .3392934    .368746     0.92   0.368    -.4298973    1.108484
              3  |  -.0385042   .4169106    -0.09   0.927    -.9081644     .831156
              4  |  -.4077269   .4784471    -0.85   0.404     -1.40575    .5902962
                 |
           nonUS |   .2827043   .3658935     0.77   0.449    -.4805362    1.045945
   highincome_sd |   .3910585   .2493597     1.57   0.133    -.1290967    .9112138
     getahead_sd |   .1493763   .2491548     0.60   0.556    -.3703515     .669104
risktakinggps_sd |  -.5976222   .2374581    -2.52   0.020    -1.092951   -.1022933
  patiencegps_sd |   .2398736   .2025095     1.18   0.250    -.1825538    .6623011
           _cons |   -1.37161   10.13294    -0.14   0.894    -22.50855    19.76533
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       122           0         122     |
  reviewyear |        10           1           9     |
        year |        11           1          10    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher

.                 sum trustsentiment_mtoe_pos [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos |  24,763  168.000001    .2156646   4.639053          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .21566459

.                 sum trustsentiment_mtoe_neg [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg |  24,763  168.000001    .6360647   7.950123          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .63606475

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  -.4531589

.                 sum trustsentiment_mtoe_abs [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs |  24,763  168.000001    .8517293   9.189717          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .85172934

.                 estadd local spec           "Neg reviews"

added macro:
               e(spec) : "Neg reviews"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE    "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      24763        122

.                 eststo col4, addscalars(nofirms r(ndistinct))
(e(nofirms) = 122 added)

. eststo col5: /// current reviews
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if ${rdreviewsample} & iscurrentjob [aw = w_rdcur], a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 8 iterations)
note: iscurrentjobflag is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: firmage is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
note: iscurrentjobflag omitted because of collinearity

HDFE Linear regression                            Number of obs   =     16,131
Absorbing 2 HDFE groups                           F(  15,     20) =     191.43
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0230
                                                  Adj R-squared   =     0.0142
                                                  Within R-sq.    =     0.0024
Number of clusters (mainethcode) =         21     Root MSE        =     9.3913

                               (Std. err. adjusted for 21 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .9691839   .4336326     2.24   0.037     .0646421    1.873726
      approveceo |   .0561358   .3795122     0.15   0.884    -.7355128    .8477845
iscurrentjobflag |          0  (omitted)
       reviewgap |          0  (omitted)
         firmage |          0  (omitted)
        firmage2 |   .0015477   .0009714     1.59   0.127    -.0004786     .003574
          gender |  -1.398341   .3396876    -4.12   0.001    -2.106917   -.6897649
             age |  -.1656403   .2767187    -0.60   0.556    -.7428655    .4115848
            age2 |   .0014999   .0023723     0.63   0.534    -.0034487    .0064485
          yrinco |  -.0246391   .0122151    -2.02   0.057    -.0501193    .0008411
                 |
       education |
              2  |  -.6682156   .9675286    -0.69   0.498    -2.686445    1.350014
              3  |   .1309423   1.017971     0.13   0.899    -1.992508    2.254393
              4  |   1.598511   .8824509     1.81   0.085    -.2422497    3.439271
                 |
           nonUS |  -.3010597   .8540943    -0.35   0.728    -2.082669     1.48055
   highincome_sd |  -.2706893   .4471131    -0.61   0.552    -1.203351    .6619722
     getahead_sd |   .0672436   .6230119     0.11   0.915    -1.232336    1.366824
risktakinggps_sd |   .6704911   .3439841     1.95   0.065    -.0470472    1.388029
  patiencegps_sd |  -.9130828   .4583196    -1.99   0.060    -1.869121    .0429551
           _cons |    .272838   12.19315     0.02   0.982    -25.16163    25.70731
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       122           0         122     |
  reviewyear |         9           1           8     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_rdcur] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos |  16,131  167.000002    .2222608   4.709359          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .22226084

.                 sum trustsentiment_mtoe_neg [aw = w_rdcur] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg |  16,131  167.000002    .6743882    8.18463          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .67438818

.                 sum trustsentiment_mtoe_abs [aw = w_rdcur] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs |  16,131  167.000002     .896649   9.426899          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .89664901

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  1.0808955

.                 estadd local spec           "Current revs"

added macro:
               e(spec) : "Current revs"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      16131        122

.                 eststo col5, addscalars(nofirms r(ndistinct))
(e(nofirms) = 122 added)

. eststo col6: /// review count > 9 & R&D review count > 4
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if RDworker & revcnt_rd > 4 & revcnt_all > 9 [aw = w_rd], ///
>         a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =     26,307
Absorbing 2 HDFE groups                           F(  17,     23) =      48.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0366
                                                  Adj R-squared   =     0.0280
                                                  Within R-sq.    =     0.0024
Number of clusters (mainethcode) =         24     Root MSE        =     8.2992

                               (Std. err. adjusted for 24 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |    .659713     .29717     2.22   0.037     .0449699    1.274456
      approveceo |   .3485295   .2731665     1.28   0.215    -.2165585    .9136175
iscurrentjobflag |  -.1357541   .3315641    -0.41   0.686    -.8216466    .5501385
       reviewgap |   .2331013   .2048364     1.14   0.267     -.190635    .6568377
         firmage |          0  (omitted)
        firmage2 |   .0018066   .0011904     1.52   0.143     -.000656    .0042692
          gender |  -.1786308   .3773862    -0.47   0.640    -.9593136    .6020521
             age |   .2936253   .1485452     1.98   0.060    -.0136638    .6009143
            age2 |  -.0024832   .0013376    -1.86   0.076    -.0052503    .0002838
          yrinco |   .0008625   .0154389     0.06   0.956    -.0310753    .0328003
                 |
       education |
              2  |   .3537939   .5172943     0.68   0.501    -.7163109    1.423899
              3  |     .32452   .6045983     0.54   0.597     -.926187    1.575227
              4  |   1.494158   .8038464     1.86   0.076    -.1687246    3.157041
                 |
           nonUS |   .1432935   .2679387     0.53   0.598    -.4109799    .6975669
   highincome_sd |  -.0055291   .2682857    -0.02   0.984    -.5605204    .5494622
     getahead_sd |   .2236133    .226018     0.99   0.333    -.2439406    .6911671
risktakinggps_sd |   .5878454   .3161972     1.86   0.076    -.0662583    1.241949
  patiencegps_sd |  -.6157019   .2893834    -2.13   0.044    -1.214337   -.0170668
           _cons |  -17.25155   7.720147    -2.23   0.035    -33.22189   -1.281209
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       208           0         208     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos |  26,307  299.000004    .1567756    3.95646          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .15677561

.                 sum trustsentiment_mtoe_neg [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg |  26,307  299.000004    .5533937   7.418572          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .55339367

.                 sum trustsentiment_mtoe_abs [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs |  26,307  299.000004    .7101693   8.397337          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .71016928

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .92895171

.                 estadd local spec           "Alt cutoff"

added macro:
               e(spec) : "Alt cutoff"

.                 estadd local cutoff1        "10"

added macro:
            e(cutoff1) : "10"

.                 estadd local cutoff2        "5"

added macro:
            e(cutoff2) : "5"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      26307        208

.                 eststo col6, addscalars(nofirms r(ndistinct))
(e(nofirms) = 208 added)

. eststo col7: /// review count > 24 & R&D review count > 9
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if RDworker & revcnt_rd > 9 & revcnt_all > 24 [aw = w_rd], ///
>         a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =     25,905
Absorbing 2 HDFE groups                           F(  17,     22) =    1536.73
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0214
                                                  Adj R-squared   =     0.0139
                                                  Within R-sq.    =     0.0015
Number of clusters (mainethcode) =         23     Root MSE        =     8.6815

                               (Std. err. adjusted for 23 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .6266661   .2965349     2.11   0.046     .0116904    1.241642
      approveceo |   .0130354   .2952625     0.04   0.965    -.5993015    .6253724
iscurrentjobflag |   .1624827   .3098544     0.52   0.605     -.480116    .8050813
       reviewgap |   .0949702   .1830525     0.52   0.609    -.2846574    .4745979
         firmage |          0  (omitted)
        firmage2 |   .0008505   .0006404     1.33   0.198    -.0004777    .0021787
          gender |  -.1281819   .3808024    -0.34   0.740    -.9179176    .6615539
             age |   .3460518    .197002     1.76   0.093    -.0625053     .754609
            age2 |  -.0029227   .0015806    -1.85   0.078    -.0062007    .0003554
          yrinco |  -.0077589   .0162605    -0.48   0.638    -.0414812    .0259634
                 |
       education |
              2  |  -.1113097   .5746587    -0.19   0.848    -1.303079    1.080459
              3  |  -.1310613   .6568571    -0.20   0.844      -1.4933    1.231177
              4  |   1.198119   .8285775     1.45   0.162    -.5202457    2.916484
                 |
           nonUS |  -.0589934    .388387    -0.15   0.881    -.8644587     .746472
   highincome_sd |  -.2165999   .2765465    -0.78   0.442    -.7901222    .3569224
     getahead_sd |  -.0171974   .3044729    -0.06   0.955    -.6486354    .6142407
risktakinggps_sd |    .743951   .3352812     2.22   0.037     .0486203    1.439282
  patiencegps_sd |  -.4755848    .288102    -1.65   0.113    -1.073072    .1219022
           _cons |  -13.30727   7.654466    -1.74   0.096    -29.18167    2.567118
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |       171           0         171     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos |  25,905  240.000003    .1953163   4.415227          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .19531629

.                 sum trustsentiment_mtoe_neg [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg |  25,905  240.000003    .5703887   7.530983          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .57038865

.                 sum trustsentiment_mtoe_abs [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs |  25,905  240.000003    .7657049   8.717059          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .76570494

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .81841719

.                 estadd local spec           "Alt cutoff"

added macro:
               e(spec) : "Alt cutoff"

.                 estadd local cutoff1        "25"

added macro:
            e(cutoff1) : "25"

.                 estadd local cutoff2        "10"

added macro:
            e(cutoff2) : "10"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      25905        171

.                 eststo col7, addscalars(nofirms r(ndistinct))
(e(nofirms) = 171 added)

. eststo col8: /// review count > 99 & R&D review count > 49
>         reghdfe trustsentiment_mtoe trust_sd ${review} ${firm} ${ceo} ${culture} ///
>         if RDworker & revcnt_rd > 49 & revcnt_all > 99 [aw = w_rd], ///
>         a(boardid reviewyear) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)
note: firmage omitted because of collinearity

HDFE Linear regression                            Number of obs   =     22,798
Absorbing 2 HDFE groups                           F(  17,     18) =     314.90
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0114
                                                  Adj R-squared   =     0.0065
                                                  Within R-sq.    =     0.0020
Number of clusters (mainethcode) =         19     Root MSE        =     9.1553

                               (Std. err. adjusted for 19 clusters in mainethcode)
----------------------------------------------------------------------------------
                 |               Robust
trustsentiment~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
        trust_sd |   .7630518    .315162     2.42   0.026      .100921    1.425183
      approveceo |   .4906844   .2484372     1.98   0.064    -.0312628    1.012632
iscurrentjobflag |   .0947546   .2454376     0.39   0.704    -.4208907       .6104
       reviewgap |   .0486162   .1001719     0.49   0.633    -.1618373    .2590696
         firmage |          0  (omitted)
        firmage2 |  -.0003131   .0007401    -0.42   0.677     -.001868    .0012418
          gender |   .8522148   .3344496     2.55   0.020     .1495622    1.554867
             age |  -.2461813   .3386998    -0.73   0.477    -.9577633    .4654006
            age2 |   .0022614    .002937     0.77   0.451     -.003909    .0084318
          yrinco |  -.0556578   .0175337    -3.17   0.005    -.0924948   -.0188208
                 |
       education |
              2  |  -1.124437   .7433327    -1.51   0.148    -2.686121    .4372467
              3  |  -.9795813   .6713413    -1.46   0.162    -2.390017    .4308544
              4  |   .2675959   .8760601     0.31   0.764    -1.572938     2.10813
                 |
           nonUS |   .8823595   .4430148     1.99   0.062      -.04838    1.813099
   highincome_sd |  -.0636979   .3602778    -0.18   0.862    -.8206135    .6932177
     getahead_sd |   .1542136   .3788616     0.41   0.689    -.6417451    .9501722
risktakinggps_sd |    1.39008    .323692     4.29   0.000     .7100278    2.070131
  patiencegps_sd |  -.1799206   .2568087    -0.70   0.493    -.7194557    .3596145
           _cons |   2.104096   11.30392     0.19   0.854    -21.64455    25.85275
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |        86           0          86     |
  reviewyear |        10           1           9     |
-----------------------------------------------------+

.                 sum trustsentiment_mtoe_pos [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_pos |  22,798         111    .2028684   4.499619          0        100

.                 estadd scalar mean_pos r(mean)

added scalar:
           e(mean_pos) =  .20286836

.                 sum trustsentiment_mtoe_neg [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_neg |  22,798         111    .6427359    7.99145          0        100

.                 estadd scalar mean_neg r(mean)

added scalar:
           e(mean_neg) =  .64273589

.                 sum trustsentiment_mtoe_abs [aw = w_rd] if e(sample)

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
trusts~e_abs |  22,798         111    .8456042   9.156913          0        100

.                 estadd scalar mean_abs r(mean)

added scalar:
           e(mean_abs) =  .84560425

.                 estadd scalar ratio _b[trust_sd]/r(mean)

added scalar:
              e(ratio) =  .90237465

.                 estadd local spec           "Alt cutoff"

added macro:
               e(spec) : "Alt cutoff"

.                 estadd local cutoff1        "100"

added macro:
            e(cutoff1) : "100"

.                 estadd local cutoff2        "50"

added macro:
            e(cutoff2) : "50"

.                 estadd local controls       "X"

added macro:
           e(controls) : "X"

.                 estadd local baselineFE     "X"

added macro:
         e(baselineFE) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      22798         86

.                 eststo col8, addscalars(nofirms r(ndistinct))
(e(nofirms) = 86 added)

. 
. esttab /*using "Table A16_Corporate trust culture robustness checks_Panel B.tex"*/, ///
>         cells(b(star fmt(3)) se(par fmt(3))) starlevels(* 0.1 ** 0.05 *** 0.01) ///
>         nomtitle collabels(none) label varwidth(23) modelwidth(12) ///
>         mgroups("Employee review's top down trust sentiment", pattern(1 0 0 0 0 0 0 0) ///
>                 prefix(\multicolumn{@span}{c}{) suffix(}) span) ///
>         keep(trust_sd) ///
>         stats(mean_pos mean_neg ratio spec cutoff1 cutoff2 controls baselineFE yearFE N nofirms, ///
>                 fmt(%9.3fc %9.3fc %9.3fc %9.0fc %9.0fc) ///
>                 lab("Positive reviews" "Negative reviews" "Normalized effect size" ///
>                         "Specification" "Minimum review count" "Min. R\&D review count" ///
>                         "Baseline controls" "Firm \& Review year FEs" "Year being reviewed FEs" "Observations" "Firms"))

-------------------------------------------------------------------------------------------------------------------------------------------------------
                        \multicolumn{8}{c}{Employee review's top down trust sentiment}                                                                 
                                 (1)             (2)             (3)             (4)             (5)             (6)             (7)             (8)   
-------------------------------------------------------------------------------------------------------------------------------------------------------
CEO's inherited gener~s        0.776***        0.705***        0.417***       -0.288           0.969**         0.660**         0.627**         0.763** 
                             (0.259)         (0.232)         (0.112)         (0.226)         (0.434)         (0.297)         (0.297)         (0.315)   
-------------------------------------------------------------------------------------------------------------------------------------------------------
Positive reviews               0.216           0.216           0.216           0.216           0.222           0.157           0.195           0.203   
Negative reviews               0.636           0.636           0.636           0.636           0.674           0.553           0.570           0.643   
Normalized effect size         0.911           0.828           1.934          -0.453           1.081           0.929           0.818           0.902   
Specification               Baseline         Add FEs     Pos reviews     Neg reviews    Current revs      Alt cutoff      Alt cutoff      Alt cutoff   
Minimum review count                                                                                              10              25             100   
Min. R\&D review count                                                                                             5              10              50   
Baseline controls                  X               X               X               X               X               X               X               X   
Firm \& Review year FEs            X               X               X               X               X               X               X               X   
Year being reviewed FEs                            X                                                                                                   
Observations                  24,763          24,763          24,763          24,763          16,131          26,307          25,905          22,798   
Firms                            122             122             276             122             122             208             171              86   
-------------------------------------------------------------------------------------------------------------------------------------------------------

. 
. cap log close
